

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta name="Description" content="scikit-learn: machine learning in Python">

  
  <title>Importance of Feature Scaling &mdash; scikit-learn 0.22 documentation</title>
  
  <link rel="canonical" href="http://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html" />

  
  <link rel="shortcut icon" href="../../_static/favicon.ico"/>
  

  <link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script> 
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid sk-docs-container px-0">
      <a class="navbar-brand py-0" href="../../index.html">
        <img
          class="sk-brand-img"
          src="../../_static/scikit-learn-logo-small.png"
          alt="logo"/>
      </a>
    <button
      id="sk-navbar-toggler"
      class="navbar-toggler"
      type="button"
      data-toggle="collapse"
      data-target="#navbarSupportedContent"
      aria-controls="navbarSupportedContent"
      aria-expanded="false"
      aria-label="Toggle navigation"
    >
      <span class="navbar-toggler-icon"></span>
    </button>

    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav mr-auto">
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../install.html">Install</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../modules/classes.html">API</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../index.html">Examples</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html">Getting Started</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html">Tutorial</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html">Glossary</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../developers/index.html">Development</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html">FAQ</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html">Related packages</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html">Roadmap</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html">About us</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
        </li>
        <li class="nav-item dropdown nav-more-item-dropdown">
          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
              <a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html">Getting Started</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html">Tutorial</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html">Glossary</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../developers/index.html">Development</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html">FAQ</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html">Related packages</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html">Roadmap</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../about.html">About us</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
          </div>
        </li>
      </ul>
      <div id="searchbox" role="search">
          <div class="searchformwrapper">
          <form class="search" action="../../search.html" method="get">
            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
            <input class="sk-search-text-btn" type="submit" value="Go" />
          </form>
          </div>
      </div>
    </div>
  </div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
    <div id="sk-sidebar-wrapper" class="border-right">
      <div class="sk-sidebar-toc-wrapper">
        <div class="sk-sidebar-toc-logo">
          <a href="../../index.html">
            <img
              class="sk-brand-img"
              src="../../_static/scikit-learn-logo-small.png"
              alt="logo"/>
          </a>
        </div>
        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
            <a href="plot_discretization_strategies.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Demonstrating the different strategies of KBinsDiscretizer">Prev</a><a href="../index.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Examples">Up</a>
            <a href="plot_map_data_to_normal.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="Map data to a normal distribution">Next</a>
        </div>
        <div class="alert alert-danger p-1 mb-2" role="alert">
          <p class="text-center mb-0">
          <strong>scikit-learn 0.22</strong><br/>
          <a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
          </p>
        </div>
        <div class="alert alert-warning p-1 mb-2" role="alert">
          <p class="text-center mb-0">
            Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
          </p>
        </div>
          <div class="sk-sidebar-toc">
            <ul>
<li><a class="reference internal" href="#">Importance of Feature Scaling</a></li>
</ul>

          </div>
      </div>
    </div>
    <div id="sk-page-content-wrapper">
      <div class="sk-page-content container-fluid body px-md-3" role="main">
        
  <div class="sphx-glr-download-link-note admonition note">
<p class="admonition-title">Note</p>
<p>Click <a class="reference internal" href="#sphx-glr-download-auto-examples-preprocessing-plot-scaling-importance-py"><span class="std std-ref">here</span></a> to download the full example code or to run this example in your browser via Binder</p>
</div>
<div class="sphx-glr-example-title section" id="importance-of-feature-scaling">
<span id="sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py"></span><h1>Importance of Feature Scaling<a class="headerlink" href="#importance-of-feature-scaling" title="Permalink to this headline">¶</a></h1>
<p>Feature scaling through standardization (or Z-score normalization)
can be an important preprocessing step for many machine learning
algorithms. Standardization involves rescaling the features such
that they have the properties of a standard normal distribution
with a mean of zero and a standard deviation of one.</p>
<p>While many algorithms (such as SVM, K-nearest neighbors, and logistic
regression) require features to be normalized, intuitively we can
think of Principle Component Analysis (PCA) as being a prime example
of when normalization is important. In PCA we are interested in the
components that maximize the variance. If one component (e.g. human
height) varies less than another (e.g. weight) because of their
respective scales (meters vs. kilos), PCA might determine that the
direction of maximal variance more closely corresponds with the
‘weight’ axis, if those features are not scaled. As a change in
height of one meter can be considered much more important than the
change in weight of one kilogram, this is clearly incorrect.</p>
<p>To illustrate this, PCA is performed comparing the use of data with
<a class="reference internal" href="../../modules/generated/sklearn.preprocessing.StandardScaler.html#sklearn.preprocessing.StandardScaler" title="sklearn.preprocessing.StandardScaler"><code class="xref py py-class docutils literal notranslate"><span class="pre">StandardScaler</span></code></a> applied,
to unscaled data. The results are visualized and a clear difference noted.
The 1st principal component in the unscaled set can be seen. It can be seen
that feature #13 dominates the direction, being a whole two orders of
magnitude above the other features. This is contrasted when observing
the principal component for the scaled version of the data. In the scaled
version, the orders of magnitude are roughly the same across all the features.</p>
<p>The dataset used is the Wine Dataset available at UCI. This dataset
has continuous features that are heterogeneous in scale due to differing
properties that they measure (i.e alcohol content, and malic acid).</p>
<p>The transformed data is then used to train a naive Bayes classifier, and a
clear difference in prediction accuracies is observed wherein the dataset
which is scaled before PCA vastly outperforms the unscaled version.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn.preprocessing</span> <span class="kn">import</span> <span class="n">StandardScaler</span>
<span class="kn">from</span> <span class="nn">sklearn.decomposition</span> <span class="kn">import</span> <span class="n">PCA</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">metrics</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="kn">from</span> <span class="nn">sklearn.pipeline</span> <span class="kn">import</span> <span class="n">make_pipeline</span>
<span class="nb">print</span><span class="p">(</span><span class="vm">__doc__</span><span class="p">)</span>

<span class="c1"># Code source: Tyler Lanigan &lt;tylerlanigan@gmail.com&gt;</span>
<span class="c1">#              Sebastian Raschka &lt;mail@sebastianraschka.com&gt;</span>

<span class="c1"># License: BSD 3 clause</span>

<span class="n">RANDOM_STATE</span> <span class="o">=</span> <span class="mi">42</span>
<span class="n">FIG_SIZE</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">7</span><span class="p">)</span>


<span class="n">features</span><span class="p">,</span> <span class="n">target</span> <span class="o">=</span> <span class="n">load_wine</span><span class="p">(</span><span class="n">return_X_y</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>

<span class="c1"># Make a train/test split using 30% test size</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">features</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span>
                                                    <span class="n">test_size</span><span class="o">=</span><span class="mf">0.30</span><span class="p">,</span>
                                                    <span class="n">random_state</span><span class="o">=</span><span class="n">RANDOM_STATE</span><span class="p">)</span>

<span class="c1"># Fit to data and predict using pipelined GNB and PCA.</span>
<span class="n">unscaled_clf</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">GaussianNB</span><span class="p">())</span>
<span class="n">unscaled_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">pred_test</span> <span class="o">=</span> <span class="n">unscaled_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># Fit to data and predict using pipelined scaling, GNB and PCA.</span>
<span class="n">std_clf</span> <span class="o">=</span> <span class="n">make_pipeline</span><span class="p">(</span><span class="n">StandardScaler</span><span class="p">(),</span> <span class="n">PCA</span><span class="p">(</span><span class="n">n_components</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">GaussianNB</span><span class="p">())</span>
<span class="n">std_clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="n">pred_test_std</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>

<span class="c1"># Show prediction accuracies in scaled and unscaled data.</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Prediction accuracy for the normal test dataset with PCA&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:.2%}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test</span><span class="p">)))</span>

<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">Prediction accuracy for the standardized test dataset with PCA&#39;</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">{:.2%}</span><span class="se">\n</span><span class="s1">&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">metrics</span><span class="o">.</span><span class="n">accuracy_score</span><span class="p">(</span><span class="n">y_test</span><span class="p">,</span> <span class="n">pred_test_std</span><span class="p">)))</span>

<span class="c1"># Extract PCA from pipeline</span>
<span class="n">pca</span> <span class="o">=</span> <span class="n">unscaled_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;pca&#39;</span><span class="p">]</span>
<span class="n">pca_std</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;pca&#39;</span><span class="p">]</span>

<span class="c1"># Show first principal components</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">PC 1 without scaling:</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">pca</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">PC 1 with scaling:</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">,</span> <span class="n">pca_std</span><span class="o">.</span><span class="n">components_</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>

<span class="c1"># Use PCA without and with scale on X_train data for visualization.</span>
<span class="n">X_train_transformed</span> <span class="o">=</span> <span class="n">pca</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">)</span>
<span class="n">scaler</span> <span class="o">=</span> <span class="n">std_clf</span><span class="o">.</span><span class="n">named_steps</span><span class="p">[</span><span class="s1">&#39;standardscaler&#39;</span><span class="p">]</span>
<span class="n">X_train_std_transformed</span> <span class="o">=</span> <span class="n">pca_std</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">scaler</span><span class="o">.</span><span class="n">transform</span><span class="p">(</span><span class="n">X_train</span><span class="p">))</span>

<span class="c1"># visualize standardized vs. untouched dataset with PCA performed</span>
<span class="n">fig</span><span class="p">,</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">)</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">ncols</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">FIG_SIZE</span><span class="p">)</span>


<span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;^&#39;</span><span class="p">,</span> <span class="s1">&#39;s&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">)):</span>
    <span class="n">ax1</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train_transformed</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="n">X_train_transformed</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
                <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="s1">&#39;class </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">l</span><span class="p">,</span>
                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
                <span class="n">marker</span><span class="o">=</span><span class="n">m</span>
                <span class="p">)</span>

<span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">c</span><span class="p">,</span> <span class="n">m</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;blue&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">,</span> <span class="s1">&#39;green&#39;</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;^&#39;</span><span class="p">,</span> <span class="s1">&#39;s&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">)):</span>
    <span class="n">ax2</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train_std_transformed</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">0</span><span class="p">],</span>
                <span class="n">X_train_std_transformed</span><span class="p">[</span><span class="n">y_train</span> <span class="o">==</span> <span class="n">l</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span>
                <span class="n">color</span><span class="o">=</span><span class="n">c</span><span class="p">,</span>
                <span class="n">label</span><span class="o">=</span><span class="s1">&#39;class </span><span class="si">%s</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">l</span><span class="p">,</span>
                <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span>
                <span class="n">marker</span><span class="o">=</span><span class="n">m</span>
                <span class="p">)</span>

<span class="n">ax1</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Training dataset after PCA&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Standardized training dataset after PCA&#39;</span><span class="p">)</span>

<span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="p">(</span><span class="n">ax1</span><span class="p">,</span> <span class="n">ax2</span><span class="p">):</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;1st principal component&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;2nd principal component&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;upper right&#39;</span><span class="p">)</span>
    <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<p class="sphx-glr-timing"><strong>Total running time of the script:</strong> ( 0 minutes  0.000 seconds)</p>
<div class="sphx-glr-footer class sphx-glr-footer-example docutils container" id="sphx-glr-download-auto-examples-preprocessing-plot-scaling-importance-py">
<div class="binder-badge docutils container">
<a class="reference external image-reference" href="https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/preprocessing/plot_scaling_importance.ipynb"><img alt="https://mybinder.org/badge_logo.svg" src="https://mybinder.org/badge_logo.svg" width="150px" /></a>
</div>
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/cf70421d0921c09068084ae7500b3849/plot_scaling_importance.py"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Python</span> <span class="pre">source</span> <span class="pre">code:</span> <span class="pre">plot_scaling_importance.py</span></code></a></p>
</div>
<div class="sphx-glr-download docutils container">
<p><a class="reference download internal" download="" href="../../_downloads/254b4e36a090f77ce573ef4d52c136e5/plot_scaling_importance.ipynb"><code class="xref download docutils literal notranslate"><span class="pre">Download</span> <span class="pre">Jupyter</span> <span class="pre">notebook:</span> <span class="pre">plot_scaling_importance.ipynb</span></code></a></p>
</div>
</div>
<p class="sphx-glr-signature"><a class="reference external" href="https://sphinx-gallery.github.io">Gallery generated by Sphinx-Gallery</a></p>
</div>


      </div>
    <div class="container">
      <footer class="sk-content-footer">
            &copy; 2007 - 2019, scikit-learn developers (BSD License).
          <a href="../../_sources/auto_examples/preprocessing/plot_scaling_importance.rst.txt" rel="nofollow">Show this page source</a>
      </footer>
    </div>
  </div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>

<script>
    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
    ga('create', 'UA-22606712-2', 'auto');
    ga('set', 'anonymizeIp', true);
    ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>


<script>
$(document).ready(function() {
    /* Add a [>>>] button on the top-right corner of code samples to hide
     * the >>> and ... prompts and the output and thus make the code
     * copyable. */
    var div = $('.highlight-python .highlight,' +
                '.highlight-python3 .highlight,' +
                '.highlight-pycon .highlight,' +
		'.highlight-default .highlight')
    var pre = div.find('pre');

    // get the styles from the current theme
    pre.parent().parent().css('position', 'relative');
    var hide_text = 'Hide prompts and outputs';
    var show_text = 'Show prompts and outputs';

    // create and add the button to all the code blocks that contain >>>
    div.each(function(index) {
        var jthis = $(this);
        if (jthis.find('.gp').length > 0) {
            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
            jthis.prepend(button);
        }
        // tracebacks (.gt) contain bare text elements that need to be
        // wrapped in a span to work with .nextUntil() (see later)
        jthis.find('pre:has(.gt)').contents().filter(function() {
            return ((this.nodeType == 3) && (this.data.trim().length > 0));
        }).wrap('<span>');
    });

    // define the behavior of the button when it's clicked
    $('.copybutton').click(function(e){
        e.preventDefault();
        var button = $(this);
        if (button.data('hidden') === 'false') {
            // hide the code output
            button.parent().find('.go, .gp, .gt').hide();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
            button.css('text-decoration', 'line-through');
            button.attr('title', show_text);
            button.data('hidden', 'true');
        } else {
            // show the code output
            button.parent().find('.go, .gp, .gt').show();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
            button.css('text-decoration', 'none');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
        }
    });

	/*** Add permalink buttons next to glossary terms ***/
	$('dl.glossary > dt[id]').append(function() {
		return ('<a class="headerlink" href="#' +
			    this.getAttribute('id') +
			    '" title="Permalink to this term">¶</a>');
	});
  /*** Hide navbar when scrolling down ***/
  // Returns true when headerlink target matches hash in url
  (function() {
    hashTargetOnTop = function() {
        var hash = window.location.hash;
        if ( hash.length < 2 ) { return false; }

        var target = document.getElementById( hash.slice(1) );
        if ( target === null ) { return false; }

        var top = target.getBoundingClientRect().top;
        return (top < 2) && (top > -2);
    };

    // Hide navbar on load if hash target is on top
    var navBar = document.getElementById("navbar");
    var navBarToggler = document.getElementById("sk-navbar-toggler");
    var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
    var $window = $(window);

    hideNavBar = function() {
        navBar.style.top = navBarHeightHidden;
    };

    showNavBar = function() {
        navBar.style.top = "0";
    }

    if (hashTargetOnTop()) {
        hideNavBar()
    }

    var prevScrollpos = window.pageYOffset;
    hideOnScroll = function(lastScrollTop) {
        if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
            return;
        }
        if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
            hideNavBar()
        } else {
            showNavBar()
        }
        prevScrollpos = lastScrollTop;
    };

    /*** high preformance scroll event listener***/
    var raf = window.requestAnimationFrame ||
        window.webkitRequestAnimationFrame ||
        window.mozRequestAnimationFrame ||
        window.msRequestAnimationFrame ||
        window.oRequestAnimationFrame;
    var lastScrollTop = $window.scrollTop();

    if (raf) {
        loop();
    }

    function loop() {
        var scrollTop = $window.scrollTop();
        if (lastScrollTop === scrollTop) {
            raf(loop);
            return;
        } else {
            lastScrollTop = scrollTop;
            hideOnScroll(lastScrollTop);
            raf(loop);
        }
    }
  })();
});

</script>
    
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
    
</body>
</html>